Global and adaptive K-nearest neighbor graphs in a spectral target detector based on Schroedinger Eigenmaps

Journal Article · Mon Dec 04 00:00:00 EST 2017 · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Proceedings (Online)

DOI:https://doi.org/10.1109/IGARSS.2017.8127208· OSTI ID:1581649

Dorado-Munoz, Leidy P. ^[1]; Messinger, David W. ^[2]

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1. Rochester Inst. of Technology, Rochester, NY (United States); Rochester Institute of Technology
2. Rochester Inst. of Technology, Rochester, NY (United States)

The Schroedinger Eigenmaps (SE) embedding has been previously introduced and applied to spectral target detection problems in hyperspectral imagery (HSI). The proposed SEbased detection approach combines the spectral and spatial connectivity of target-like pixels into the Schroedinger operator by using a “knowledge propagation” scheme. Likewise, it has been noted the impact that the local data structure modeling has over the SE-based detector. This local data structure is modeled by using an adjacency graph, i.e, set of vertices and edges, that in most of the applications is a k nearest neighbor (knn) graph. In this paper, the impact of k in the graph-building process is analyzed and assessed from a target detection point of view. Here, two ways to estimate the parameter k in the graph construction are considered and their assessment is performed by using two HSI data sets and the Receiver Optimal Curve (ROC) as a detection performance metric

• Research Organization: Rochester Inst. of Technology, Rochester, NY (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation (NA-20)
• Grant/Contract Number: NA0002482
• OSTI ID: 1581649
• Journal Information: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Proceedings (Online), Journal Name: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Proceedings (Online) Vol. 2017; ISSN 2153-7003
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Schroedinger eigenmaps
graph-based embedding
k nearest neighbor
spectral target detection